Gastos Casa
30 September, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/7/2024 | Comida | 27300 | Andrés | NA |
| 28/7/2024 | Comida | 67250 | Tami | Supermercado |
| 30/7/2024 | Comida | 30650 | Andrés | piwén |
| 1/8/2024 | Electricidad | 209425 | Andrés | NA |
| 3/8/2024 | Comida | 45130 | Tami | Supermercado |
| 3/8/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 11/8/2024 | Comida | 8230 | Tami | Supermercado |
| 8/8/2024 | Comida | 5000 | Andrés | platano y cosas |
| 15/8/2024 | Electricidad | 190084 | Andrés | enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador) |
| 18/8/2024 | VTR | 21990 | Andrés | NA |
| 16/8/2024 | Comida | 11756 | Andrés | lider |
| 19/8/2024 | Comida | 91135 | Tami | Supermercado |
| 23/8/2024 | Otros | 170000 | Andrés | instalacion |
| 23/8/2024 | Otros | 285000 | Andrés | aire |
| 25/8/2024 | Gas | 75000 | Andrés | NA |
| 25/8/2024 | Comida | 66781 | Tami | Supermercado |
| 29/8/2024 | Comida | 45000 | Andrés | piwen |
| 2/9/2024 | Comida | 42057 | Tami | Supermercado |
| 4/9/2024 | Agua | 11723 | Andrés | NA |
| 4/9/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 7/9/2024 | Comida | 15960 | Tami | Supermercado |
| 8/9/2024 | Gas/Bencina | 20182 | Tami | Parafina |
| 8/9/2024 | Comida | 55162 | Tami | Supermercado |
| 14/9/2024 | Comida | 16000 | Andrés | NA |
| 14/9/2024 | Comida | 59823 | Tami | Supermercado |
| 20/9/2024 | Electricidad | 22000 | Andrés | NA |
| 23/9/2024 | Comida | 60298 | Tami | Supermercado |
| 26/9/2024 | Rgalo Chepa | 31036 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.8632e+08 2 6.8387 0.0011 **
## lag_depvar 1.5071e+11 1 2325.6945 <2e-16 ***
## Residuals 4.9185e+10 759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -8.325119 14466.00 0.0503408
## 2-0 30464.943 23932.015922 36997.87 0.0000000
## 2-1 23236.105 19431.319722 27040.89 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
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## 275 69999.29 2 58926.43
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## 301 87702.86 2 89765.00
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## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
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## 603 55402.57 2 53250.43
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## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
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## 610 58262.71 2 60483.43
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## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
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## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 607 52699.21 17634.641
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1988.46987 4027.33828 -525.09915 2449.31775 -2943.97896 528.07012
## 8 9 10 11 12 13
## -5642.13918 -1203.17598 -3983.10388 -451.10514 -4968.11280 -1657.69791
## 14 15 16 17 18 19
## -947.71286 333.58910 -3276.39793 -421.62760 -2167.50344 6562.94093
## 20 21 22 23 24 25
## -1527.44492 -1212.13894 1468.59074 -1182.14618 235.00059 1699.33814
## 26 27 28 29 30 31
## -7086.45630 926.32409 8181.78765 455.42373 23.67100 -2364.83413
## 32 33 34 35 36 37
## 1597.58539 4602.47943 1180.34605 2447.06236 -1803.74383 4657.08392
## 38 39 40 41 42 43
## 4332.74978 -2226.93047 -2950.63822 -1098.81786 -10737.20313 7236.21780
## 44 45 46 47 48 49
## 2549.76858 1374.12145 8119.02340 741.58281 6581.09983 6795.08807
## 50 51 52 53 54 55
## -5775.87310 -4733.39520 -5030.72658 -7929.84574 6086.82295 -4081.38014
## 56 57 58 59 60 61
## -4919.92107 3807.66180 866.45383 -45.86821 130.25406 -5005.91854
## 62 63 64 65 66 67
## 18092.17368 3706.94804 -3568.57543 5974.04366 7418.38374 14743.22614
## 68 69 70 71 72 73
## 1862.55631 -13055.32812 -1238.33887 4696.20385 -4829.23825 -4368.08705
## 74 75 76 77 78 79
## -10488.56936 2419.49899 -5427.60511 1011.30246 -6906.07573 476.24087
## 80 81 82 83 84 85
## -2413.05186 -2757.01470 -4000.89228 -618.19075 2241.65750 3711.44922
## 86 87 88 89 90 91
## 452.22109 -503.39637 177.78798 4286.73599 -1153.38572 1153.35181
## 92 93 94 95 96 97
## -2055.98132 -1047.52725 169.49123 268.88889 -7487.50099 2349.61608
## 98 99 100 101 102 103
## -8626.40080 -3006.30578 -4111.86145 -1820.68965 -1343.29710 3103.20602
## 104 105 106 107 108 109
## -2392.90227 2537.61431 -1193.75657 933.78145 2560.42898 -3163.82345
## 110 111 112 113 114 115
## -4747.58788 -896.19536 1859.24960 11665.16974 -1205.67521 2694.53088
## 116 117 118 119 120 121
## 4299.88805 3557.78338 -1033.03830 -4663.30782 -3702.20220 2319.68635
## 122 123 124 125 126 127
## -1720.18009 1342.56222 8867.50339 902.09879 183.29734 -2474.06259
## 128 129 130 131 132 133
## 2683.39673 7091.57035 1083.97429 -8431.24892 1764.37487 4158.27011
## 134 135 136 137 138 139
## -3122.02778 -1399.43125 -843.39626 -3874.94005 1167.39838 -502.61909
## 140 141 142 143 144 145
## -2922.14202 1695.69780 -1891.38572 -7847.90026 1982.36360 -3518.61041
## 146 147 148 149 150 151
## 2050.21278 -291.65573 992.02900 -380.79599 1331.74524 1176.08768
## 152 153 154 155 156 157
## 3353.81981 -4846.33865 -1186.25966 -3252.02478 5925.81151 9751.16594
## 158 159 160 161 162 163
## -3596.29490 -4952.55613 3414.21382 35.22149 2545.03569 -6041.52060
## 164 165 166 167 168 169
## -6905.50866 3970.75942 17238.27593 3562.09231 -452.74779 -2509.67534
## 170 171 172 173 174 175
## -1186.04887 3499.21747 -304.96454 -8158.10099 2737.61253 4214.62724
## 176 177 178 179 180 181
## 534.94830 8659.53703 -9300.59615 -3576.78055 -10867.97405 -11414.62777
## 182 183 184 185 186 187
## 1013.38306 9091.00884 -1577.08271 5778.06021 6435.95500 13067.58297
## 188 189 190 191 192 193
## 8394.02806 -4076.31068 2414.17281 10315.42291 -1663.51756 -2489.96853
## 194 195 196 197 198 199
## -10351.39343 -6490.94392 1075.01716 -5383.94535 -9968.55362 5172.17782
## 200 201 202 203 204 205
## -3244.76287 -1896.78360 -989.26109 6312.43259 9733.20142 471.10579
## 206 207 208 209 210 211
## 2813.79869 2994.34140 5687.92797 12755.12544 -5719.95630 -11368.59208
## 212 213 214 215 216 217
## -5797.00441 -10743.53715 -5274.23317 1314.83277 -13205.06308 16147.35261
## 218 219 220 221 222 223
## 7651.75194 1405.50065 26568.92981 12514.16082 7355.41055 14053.70605
## 224 225 226 227 228 229
## -3852.68979 -1725.79274 3762.68422 343.48931 2714.38942 8970.64279
## 230 231 232 233 234 235
## 5822.59385 -1903.68701 -1851.01788 9380.35787 -11525.33172 -7365.94876
## 236 237 238 239 240 241
## -8662.55150 -10261.93124 2876.80970 1173.30611 -8464.18702 -9187.70192
## 242 243 244 245 246 247
## 8866.93790 -7942.08190 2283.21236 -10485.55853 -4275.37475 1195.60270
## 248 249 250 251 252 253
## 793.92141 -12511.43612 3403.76052 1851.43940 4018.46245 1965.29440
## 254 255 256 257 258 259
## -1318.23577 10977.98079 20765.74027 3157.50715 -4316.03742 4031.09207
## 260 261 262 263 264 265
## -1768.35624 3644.53478 -4938.99857 -11010.17461 -4894.08673 -704.58867
## 266 267 268 269 270 271
## -5369.50630 8579.64941 -4440.11056 4012.22023 -2266.65274 4261.93661
## 272 273 274 275 276 277
## 556.83457 7150.80661 -1540.14258 11884.86645 -4686.86933 1593.81382
## 278 279 280 281 282 283
## -504.86707 7710.56445 -5176.12682 -2876.11093 -11419.45396 -2867.15452
## 284 285 286 287 288 289
## 18453.02196 7650.34988 2620.78350 -742.46019 780.37730 6267.76174
## 290 291 292 293 294 295
## 6765.95505 -18875.50387 -11311.78881 -8326.48276 9443.17659 2892.15865
## 296 297 298 299 300 301
## -1345.30845 27233.79017 9982.48252 4835.55507 9451.73629 2803.66033
## 302 303 304 305 306 307
## -1094.26974 7815.47089 -24365.42363 -3688.56726 -337.75352 -7127.94010
## 308 309 310 311 312 313
## -4148.23363 2751.13785 -9357.21259 -3415.07819 -8370.08807 1369.40486
## 314 315 316 317 318 319
## -3330.53581 1869.41768 -4247.31928 27275.09754 -834.35837 3170.64317
## 320 321 322 323 324 325
## 10712.09908 5492.77948 32288.87634 5096.43836 -20958.54404 1696.29737
## 326 327 328 329 330 331
## 1011.11484 -6568.78566 -1861.01517 -33400.91297 699.41434 -2463.83179
## 332 333 334 335 336 337
## -245.12120 -3306.30778 3950.78796 -551.83164 -7061.54802 -3235.68481
## 338 339 340 341 342 343
## -2309.61206 -7794.12294 3727.33893 -1477.79933 -1841.54231 -1095.93699
## 344 345 346 347 348 349
## 77.71024 387.74031 -1708.08824 -9537.74390 -13320.28303 2176.08698
## 350 351 352 353 354 355
## -4441.79459 -3778.61788 -6099.59217 1626.44577 1272.09324 2649.03665
## 356 357 358 359 360 361
## -3862.13800 -618.79976 576.13372 6915.64266 193.04971 -123.52650
## 362 363 364 365 366 367
## 2495.67729 -2834.23687 -967.93149 -8835.08766 -4731.41035 -6320.66860
## 368 369 370 371 372 373
## -5062.38774 -7366.65156 4895.83833 267.66699 7020.80849 -7720.57372
## 374 375 376 377 378 379
## -2359.58156 -3484.34337 -2564.87903 -12554.61568 1789.14688 -10736.78415
## 380 381 382 383 384 385
## 5580.91306 9237.47860 3045.12058 -2476.49129 1520.25575 6660.53788
## 386 387 388 389 390 391
## 11334.55170 -5868.06667 -5450.77986 -261.20944 8456.39387 1718.16785
## 392 393 394 395 396 397
## 11121.35499 -9969.17106 2656.57676 595.07849 441.59728 -777.35783
## 398 399 400 401 402 403
## -690.62301 -14617.55591 8376.71328 -1304.15786 -1493.71944 6862.14501
## 404 405 406 407 408 409
## -8039.71712 -1413.37075 -2644.03009 -5928.83591 -2969.33584 -4023.15096
## 410 411 412 413 414 415
## -8858.93364 6025.76630 1547.91599 -7460.71959 -7786.30288 14122.13237
## 416 417 418 419 420 421
## 3736.78050 4412.07166 -8115.92389 -4837.04806 -2697.04409 2725.14071
## 422 423 424 425 426 427
## -14098.44038 -2893.60409 -9196.78280 2910.82932 6886.15272 6496.03709
## 428 429 430 431 432 433
## -4059.66326 -4199.26862 -4804.66990 -1875.38117 -5796.34989 -6714.95483
## 434 435 436 437 438 439
## -6041.78113 -1487.30042 -937.26619 -5060.39197 2494.33396 4759.90157
## 440 441 442 443 444 445
## -5128.12011 -2235.55439 1498.48832 -3910.80552 2758.22334 -6649.84487
## 446 447 448 449 450 451
## -12189.97026 -4604.19022 9553.00872 -2103.80257 4682.27376 -5932.44935
## 452 453 454 455 456 457
## -1193.14631 314.32921 2959.62015 -12329.27332 3290.23884 -6770.87156
## 458 459 460 461 462 463
## 6444.82331 2948.56973 2448.56971 -3901.07409 2030.48366 -66.26236
## 464 465 466 467 468 469
## 1733.35122 -578.88751 3290.65163 -2694.92634 5744.16099 -6994.27948
## 470 471 472 473 474 475
## -3027.56361 -2270.65644 -4729.27814 2927.16332 7736.40720 -6064.46636
## 476 477 478 479 480 481
## 1426.72661 -6232.53207 -2906.73753 1948.18213 -12987.21433 -9832.36842
## 482 483 484 485 486 487
## -1290.54975 -62.09947 -1037.71674 -1413.49707 -9653.86293 11013.73746
## 488 489 490 491 492 493
## 6179.36100 7376.34094 -5469.36924 5321.92526 9255.37361 6028.41892
## 494 495 496 497 498 499
## -13495.01816 -10615.96168 -3508.43141 -1174.64453 -590.29298 -7688.52068
## 500 501 502 503 504 505
## 539.66736 4223.52943 5456.47365 619.61829 40.55657 -7279.37679
## 506 507 508 509 510 511
## 516.85383 -5097.92728 1775.99579 -1346.49092 -8208.22808 -664.91301
## 512 513 514 515 516 517
## -2733.84422 -647.97253 1274.97966 -9546.59179 -7831.61945 24210.85195
## 518 519 520 521 522 523
## 9799.46834 5867.10251 -5344.61230 2772.03738 16992.89545 11471.47471
## 524 525 526 527 528 529
## -24142.20785 -5107.82116 -3791.37300 4508.46054 -410.71312 -11157.57374
## 530 531 532 533 534 535
## 4317.49123 13847.68344 -5013.10667 4324.36965 5511.23327 -1827.95499
## 536 537 538 539 540 541
## -4586.06447 -7129.42913 -2167.68251 8255.05032 77.46133 -8191.52497
## 542 543 544 545 546 547
## 1751.59330 -657.78608 308.84436 -11085.88800 -11134.49214 1953.63359
## 548 549 550 551 552 553
## 6928.29240 -1374.43543 780.29707 -7773.28154 8500.58103 861.59894
## 554 555 556 557 558 559
## -11987.68917 9097.10370 8611.71785 66.79381 4816.79958 -3605.94856
## 560 561 562 563 564 565
## 14065.80536 21477.13471 -6459.79686 -9699.50418 6730.64947 189.52261
## 566 567 568 569 570 571
## 3412.84768 -7417.44123 -17364.43856 6578.33819 6369.72994 1852.34590
## 572 573 574 575 576 577
## 3052.33200 1729.37639 -2203.54090 14673.18100 -9668.48882 -6288.31747
## 578 579 580 581 582 583
## 8654.81983 2818.42998 -6582.67569 7457.53042 -3837.47003 -2822.18084
## 584 585 586 587 588 589
## 15650.93641 -14522.44068 8373.11193 31.85720 -6252.29292 -805.83972
## 590 591 592 593 594 595
## 201.02812 -10701.57588 1731.18994 -7201.08096 3002.15819 8812.50014
## 596 597 598 599 600 601
## -7534.79153 5802.97034 2699.49526 6825.02207 -3209.60588 6120.83937
## 602 603 604 605 606 607
## -8317.21321 2218.03024 1237.50529 3107.90005 1470.09111 373.61675
## 608 609 610 611 612 613
## -5835.20377 8037.89760 -1204.03412 -2598.23731 -3482.76680 -8263.43498
## 614 615 616 617 618 619
## 11909.76760 4915.55063 -9326.32605 11594.06200 6042.35563 -5571.18080
## 620 621 622 623 624 625
## 26346.73473 -12784.18177 -6773.19004 3146.38652 -4164.35553 -10607.87233
## 626 627 628 629 630 631
## 11258.65550 -21645.52755 -2473.26353 8618.98985 11105.53616 -1555.46764
## 632 633 634 635 636 637
## 33276.14345 -6522.45045 5754.35093 5440.51624 -2224.39075 -5315.53829
## 638 639 640 641 642 643
## -1929.42063 -12428.74380 -2270.93439 -1917.46806 -2552.83338 -2892.93624
## 644 645 646 647 648 649
## 1777.41868 4401.65394 16948.47199 18575.54563 941.08213 4835.78191
## 650 651 652 653 654 655
## 10658.49274 20211.30534 843.85630 -27977.71323 -1335.54542 -2288.41967
## 656 657 658 659 660 661
## 1866.85353 -3186.26693 -10622.53571 1639.08238 4211.15711 -1006.54146
## 662 663 664 665 666 667
## 13031.76008 1396.40408 1849.42936 -11653.86081 1380.65810 1194.80650
## 668 669 670 671 672 673
## -5153.65346 -7410.77718 2049.02581 -3717.14808 2661.67061 -3377.93358
## 674 675 676 677 678 679
## -9342.28382 -8336.20216 -3027.76460 121.57411 2805.23498 688.00632
## 680 681 682 683 684 685
## -3843.70921 -1832.69757 -1342.43408 -8265.04813 4605.46910 -2262.81864
## 686 687 688 689 690 691
## -1420.58399 566.16202 10839.57895 9874.70038 10680.18252 -9574.88509
## 692 693 694 695 696 697
## -3503.31171 -3102.90479 5896.81872 -10339.61410 -7901.16659 -8624.16869
## 698 699 700 701 702 703
## -6310.71816 -4788.70028 3026.14456 -4436.52368 -1940.09854 4182.69098
## 704 705 706 707 708 709
## 31091.02850 9646.30945 23609.74201 1950.16330 8580.58513 23199.82472
## 710 711 712 713 714 715
## 6934.02961 -17827.56892 5077.80186 -5182.65084 112.55459 676.87014
## 716 717 718 719 720 721
## -17080.89611 -5177.64709 3394.91977 -2933.32428 -12912.50322 4283.31834
## 722 723 724 725 726 727
## -5520.84376 756.36699 -3908.08692 -12432.95876 1328.58644 -1883.64528
## 728 729 730 731 732 733
## -9790.70601 17219.18079 1828.60545 -2656.61573 5770.11343 -8543.27166
## 734 735 736 737 738 739
## -679.44321 8183.87029 -15260.28894 -5897.24595 7401.29021 -4742.94076
## 740 741 742 743 744 745
## 183.86230 1859.24556 -1908.57386 -5125.87363 6434.30436 -6213.36484
## 746 747 748 749 750 751
## 22731.88086 7980.94907 -1762.07928 -7125.36758 23533.14207 -4058.99378
## 752 753 754 755 756 757
## 1589.46323 -14234.59032 56211.20043 27313.66572 15594.97213 -10114.10038
## 758 759 760 761 762 763
## 11032.40608 7756.78648 6249.12134 -45959.70145 -16023.64424 1024.00437
## 764
## -2448.57368
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17280.82 20111.66 24341.24 24060.83 26400.69 23748.64 24460.85 19720.32
## 10 11 12 13 14 15 16 17
## 19458.39 16816.39 17589.40 14337.56 14388.43 15049.27 16736.11 15065.77
## 18 19 20 21 22 23 24 25
## 16094.50 15471.63 22513.44 21602.71 21085.55 22964.72 22294.57 22943.38
## 26 27 28 29 30 31 32 33
## 24778.74 18741.96 20458.21 28250.58 28307.90 27982.69 25625.70 27020.09
## 34 35 36 37 38 39 40 41
## 30841.08 31187.51 32588.60 30113.49 34110.25 37299.93 34372.92 31202.10
## 42 43 44 45 46 47 48 49
## 30056.49 20690.07 28165.66 30588.16 31671.12 38469.99 37967.47 42602.91
## 50 51 52 53 54 55 56 57
## 46814.87 39554.68 34154.30 29205.56 22389.32 28643.24 25243.49 21562.34
## 58 59 60 61 62 63 64 65
## 25945.40 27197.73 27493.03 27902.49 23797.11 40293.19 42126.58 37399.81
## 66 67 68 69 70 71 72 73
## 41582.62 46470.06 57077.02 55102.19 40430.05 37950.22 40950.81 35283.66
## 74 75 76 77 78 79 80 81
## 30762.00 21518.79 24701.89 20650.98 22725.08 17649.90 19653.77 18884.73
## 82 83 84 85 86 87 88 89
## 17918.04 15998.05 17268.49 20855.84 25248.21 26232.40 26257.21 26870.41
## 90 91 92 93 94 95 96 97
## 30971.81 29809.08 30802.70 28878.24 28082.65 28448.68 28852.93 22467.24
## 98 99 100 101 102 103 104 105
## 25464.97 18535.45 17398.15 15450.12 15748.15 16421.65 20868.62 19957.39
## 106 107 108 109 110 111 112 113
## 23448.33 23239.50 24906.00 27766.25 25278.73 21742.62 22016.46 24647.54
## 114 115 116 117 118 119 120 121
## 35449.68 33652.90 35479.83 38460.93 40405.61 38107.31 32958.06 29320.46
## 122 123 124 125 126 127 128 129
## 31391.32 29681.15 30855.93 38412.04 38056.56 37123.49 34005.03 35776.00
## 130 131 132 133 134 135 136 137
## 41142.88 40586.39 31838.63 33096.16 36267.60 32698.86 31095.40 30185.65
## 138 139 140 141 142 143 144 145
## 26762.46 28168.76 27939.71 25639.30 27652.10 26284.76 19923.64 22936.75
## 146 147 148 149 150 151 152 153
## 20775.93 23735.94 24272.83 25854.08 26035.11 27679.77 28973.04 31987.77
## 154 155 156 157 158 159 160 161
## 27483.97 26751.17 24320.47 30180.69 41616.72 39956.56 37336.64 42328.06
## 162 163 164 165 166 167 168 169
## 43728.54 47124.81 42616.79 37950.95 43345.01 59553.48 61752.89 60176.10
## 170 171 172 173 174 175 176 177
## 57020.05 55428.50 58115.54 57145.24 49481.67 52288.94 56010.05 56046.03
## 178 179 180 181 182 183 184 185
## 63133.88 53690.78 50460.40 41321.91 32909.90 36397.99 46443.37 45902.51
## 186 187 188 189 190 191 192 193
## 51821.05 57532.99 68253.97 73506.45 67237.40 67429.72 74459.37 70160.68
## 194 195 196 197 198 199 200 201
## 65709.25 55014.94 49079.41 50495.52 46115.55 38329.39 44717.19 42954.78
## 202 203 204 205 206 207 208 209
## 42594.83 43070.42 49825.37 58663.47 58295.20 60010.09 61656.36 65425.73
## 210 211 212 213 214 215 216 217
## 74837.81 66966.16 55223.15 49862.97 40911.09 37886.31 40982.06 31059.65
## 218 219 220 221 222 223 224 225
## 47935.53 55214.21 56110.93 78745.41 86197.30 88189.01 95736.69 86739.65
## 226 227 228 229 230 231 232 233
## 80772.60 80356.94 77026.18 76192.50 80902.26 82258.69 76726.16 71966.64
## 234 235 236 237 238 239 240 241
## 77587.76 64312.38 56394.69 48391.65 40051.48 44219.27 46359.62 39847.99
## 242 243 244 245 246 247 248 249
## 33563.92 43787.22 38067.22 41980.27 34288.66 33001.97 36636.22 39443.86
## 250 251 252 253 254 255 256 257
## 30326.10 36229.99 40009.54 45174.42 47877.09 47372.59 57614.26 75010.78
## 258 259 260 261 262 263 264 265
## 74826.89 68176.05 69649.36 65891.89 67329.71 61123.32 50459.66 46509.87
## 266 267 268 269 270 271 272 273
## 46718.08 42847.21 51600.68 47895.21 52018.08 50145.49 54189.45 54483.76
## 274 275 276 277 278 279 280 281
## 60466.57 58114.42 67731.73 61691.47 61900.30 60258.86 65968.70 59735.25
## 282 283 284 285 286 287 288 289
## 56318.88 45931.30 44337.26 61470.36 66968.65 67375.75 64808.19 63900.81
## 290 291 292 293 294 295 296 297
## 67878.76 71766.50 52872.36 43031.34 37076.82 47338.84 50562.02 49681.07
## 298 299 300 301 302 303 304 305
## 73738.23 79649.44 80313.26 84899.20 83108.13 78166.96 81613.85 56657.00
## 306 307 308 309 310 311 312 313
## 52939.61 52621.23 46447.09 43672.58 47255.21 39850.22 38579.66 33172.45
## 314 315 316 317 318 319 320 321
## 36935.25 36121.30 39930.75 37926.76 63564.93 61418.50 63032.76 70984.93
## 322 323 324 325 326 327 328 329
## 73358.55 98693.85 97080.83 73049.85 71854.60 70221.36 62219.30 59358.06
## 330 331 332 333 334 335 336 337
## 29479.01 33145.40 33582.41 35889.02 35233.64 40967.55 42036.98 37311.83
## 338 339 340 341 342 343 344 345
## 36530.75 36656.69 32002.52 37967.09 38626.69 38883.65 39754.43 41530.12
## 346 347 348 349 350 351 352 353
## 43341.66 43094.74 36079.85 26701.77 32015.79 30883.33 30475.74 28105.84
## 354 355 356 357 358 359 360 361
## 32757.91 36490.68 40928.71 39128.09 40381.15 42507.36 49860.24 50407.67
## 362 363 364 365 366 367 368 369
## 50608.18 53057.24 50555.07 50002.80 42690.12 39902.95 36101.82 33893.22
## 370 371 372 373 374 375 376 377
## 29973.59 37219.76 39493.62 47334.00 41340.15 40790.49 39336.16 38871.62
## 378 379 380 381 382 383 384 385
## 29791.57 34363.36 27454.80 35627.09 45901.02 49446.06 47729.32 49709.60
## 386 387 388 389 390 391 392 393
## 55894.16 65325.35 58575.49 53075.35 52805.61 60142.98 60663.36 69282.46
## 394 395 396 397 398 399 400 401
## 58450.42 60008.35 59570.97 59057.79 57553.34 56321.98 43156.29 51692.87
## 402 403 404 405 406 407 408 409
## 50699.01 49671.14 56035.86 48620.94 47936.03 46272.26 41974.19 40811.58
## 410 411 412 413 414 415 416 417
## 38886.51 33014.38 40842.23 43751.86 38454.59 33570.87 48357.65 52180.50
## 418 419 420 421 422 423 424 425
## 56087.35 48599.48 44943.76 43627.29 47193.30 35678.46 35409.21 29700.74
## 426 427 428 429 430 431 432 433
## 35258.70 43538.82 50391.66 47175.55 44260.96 41203.67 41092.49 37590.38
## 434 435 436 437 438 439 440 441
## 33750.78 31000.59 32567.69 34406.53 32422.52 37260.96 43431.12 40201.98
## 442 443 444 445 446 447 448 449
## 39909.65 42898.95 40797.06 44763.84 40037.83 31121.19 29965.28 41257.52
## 450 451 452 453 454 455 456 457
## 40940.87 46559.88 42220.86 42568.53 44179.81 47876.84 37808.76 42630.44
## 458 459 460 461 462 463 464 465
## 38079.75 45605.72 49105.72 51711.36 48459.52 50786.98 50987.36 52724.46
## 466 467 468 469 470 471 472 473
## 52224.92 55151.93 52495.41 57517.85 50816.14 48440.66 47034.85 43678.41
## 474 475 476 477 478 479 480 481
## 47413.16 54834.04 49292.70 50986.25 45804.74 44192.96 47009.79 36484.23
## 482 483 484 485 486 487 488 489
## 30082.41 31941.10 34622.43 36103.93 37064.29 30741.26 43200.21 49822.52
## 490 491 492 493 494 495 496 497
## 56613.94 51355.50 56161.05 63751.30 67541.02 53875.53 44507.00 42543.22
## 498 499 500 501 502 503 504 505
## 42864.58 43651.23 38169.33 40554.61 45825.95 51475.24 52180.87 52290.81
## 506 507 508 509 510 511 512 513
## 46028.57 47360.93 43641.43 46381.21 46048.80 39800.34 40924.99 40104.83
## 514 515 516 517 518 519 520 521
## 41204.16 43829.16 36710.05 32016.29 55769.96 63884.18 67516.33 60933.11
## 522 523 524 525 526 527 528 529
## 62264.96 75773.24 82710.21 57803.11 52702.37 49415.54 53769.57 53278.72
## 530 531 532 533 534 535 536 537
## 43518.22 48481.60 61069.96 55622.06 59000.34 62965.38 60034.78 55093.86
## 538 539 540 541 542 543 544 545
## 48593.40 47256.95 55148.82 54900.67 47503.12 49714.07 49541.73 50231.60
## 546 547 548 549 550 551 552 553
## 40933.92 32816.22 37133.28 45203.58 45001.70 46697.85 40741.85 49703.40
## 554 555 556 557 558 559 560 561
## 50852.12 40689.61 50176.14 57994.06 57362.63 60939.81 56731.19 68424.58
## 562 563 564 565 566 567 568 569
## 85017.94 75165.50 63794.35 68188.33 66323.44 67503.30 59121.44 43201.95
## 570 571 572 573 574 575 576 577
## 50170.56 56041.94 57217.95 59281.62 59924.97 57067.82 69244.49 58678.60
## 578 579 580 581 582 583 584 585
## 52437.47 59995.57 61490.96 54624.47 60855.18 56456.61 53518.06 67010.58
## 586 587 588 589 590 591 592 593
## 52522.46 59824.71 58922.29 52680.41 51989.54 52264.00 43032.95 45813.80
## 594 595 596 597 598 599 600 601
## 40470.98 44692.50 53405.65 46775.03 52600.50 54964.69 60601.32 56781.45
## 602 603 604 605 606 607 608 609
## 61567.64 53184.54 55053.78 55825.67 58120.62 58691.38 58234.78 52445.53
## 610 611 612 613 614 615 616 617
## 59466.75 57537.95 54651.77 51376.72 44379.95 55824.31 59689.47 50676.80
## 618 619 620 621 622 623 624 625
## 61019.22 65180.18 58707.27 80807.47 66015.48 58388.76 60380.21 55760.16
## 626 627 628 629 630 631 632 633
## 46150.92 56796.96 37464.69 37325.72 46839.18 57261.75 55317.57 83881.88
## 634 635 636 637 638 639 640 641
## 74124.36 76312.48 77940.39 72696.97 65457.99 62111.60 50085.93 48463.61
## 642 643 644 645 646 647 648 649
## 47361.55 45852.51 44246.44 46907.92 51498.81 66383.74 80725.20 77865.08
## 650 651 652 653 654 655 656 657
## 78763.65 84601.41 97968.86 92757.57 63198.40 60664.85 57636.72 58615.70
## 658 659 660 661 662 663 664 665
## 55077.11 45544.92 47915.56 52208.54 51405.38 62900.74 62779.14 63067.00
## 666 667 668 669 670 671 672 673
## 51588.77 52940.48 53953.08 49318.63 43332.97 46350.43 43963.04 47429.79
## 674 675 676 677 678 679 680 681
## 45195.14 38073.92 32762.62 32760.14 35493.34 40198.14 42445.57 40461.55
## 682 683 684 685 686 687 688 689
## 40485.01 40931.19 35306.10 41599.10 41099.44 41396.98 43380.99 54027.16
## 690 691 692 693 694 695 696 697
## 62435.82 70438.74 59797.17 55827.90 52728.18 57852.61 48201.31 41936.60
## 698 699 700 701 702 703 704 705
## 35867.43 32605.41 31094.14 36569.10 34842.67 35511.45 41410.26 69904.83
## 706 707 708 709 710 711 712 713
## 76027.97 93474.12 89814.56 92394.89 107333.54 106180.85 83673.06 84018.37
## 714 715 716 717 718 719 720 721
## 75406.59 72525.99 70514.18 53343.36 48768.22 52240.18 49759.36 38937.25
## 722 723 724 725 726 727 728 729
## 44473.13 40765.92 42998.09 40885.53 31646.41 35574.36 36195.99 29868.25
## 730 731 732 733 734 735 736 737
## 47831.68 50066.33 48111.60 53732.84 46183.30 46456.27 54391.57 40921.39
## 738 739 740 741 742 743 744 745
## 37354.14 45806.23 42599.42 44093.33 46846.00 45964.30 42404.12 49352.51
## 746 747 748 749 750 751 752 753
## 44402.40 65243.34 70532.79 66664.65 58646.72 78311.14 71425.54 70351.02
## 754 755 756 757 758 759 760 761
## 55673.80 104111.48 121083.03 125645.39 107278.45 109692.64 108944.45 106985.13
## 762 763 764
## 59937.50 45075.28 46973.43
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8226
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.838734 0.493911 3.179623
## t2* 2325.694463 33.476623 339.713445
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.827543 6.965058 13.07406
## 2 lag_depvar 1842.531729 2340.533064 2954.02149Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 30 00:49:00 2024
## =-=-=-=-=
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## =-=-=-=-= Iteration 10000 Mon Sep 30 00:49:38 2024
## =-=-=-=-=
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.081250 | 5.195333 | 5.410333 | 5.849167 | 6.334807 |
| Comida | 332.209000 | 366.009167 | 312.386750 | 317.896583 | 343.810105 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 96.293625 | 38.104750 | 47.072333 | 29.523000 | 42.386053 |
| Enceres | 34.359750 | 18.259750 | 24.219750 | 14.801167 | 25.094561 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.751983 |
| Gas/Bencina | 62.041250 | 42.636000 | 45.575000 | 13.583667 | 34.125403 |
| Diosi | 38.008625 | 55.804250 | 31.180667 | 52.687833 | 42.554351 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991250 | 12.829167 | 25.156667 | 19.086917 | 19.125754 |
| Netflix | 2.087125 | 8.713833 | 7.151583 | 7.028750 | 6.849018 |
| Otros | 102.375000 | 5.481667 | 5.000000 | 0.000000 | 16.575088 |
| Total | 693.446875 | 563.738000 | 505.988083 | 474.453167 | 545.607123 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2474, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-10-09 00:04:58 sería de: 38.249 pesos// Percentil 95% más alto proyectado: 41.264,1
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37947.23 | 37946.67 |
| Lo.80 | 37952.71 | 37952.01 |
| Point.Forecast | 38249.38 | 39073.92 |
| Hi.80 | 39961.78 | 43859.49 |
| Hi.95 | 40899.08 | 46392.82 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(3,1,0)
##
## Coefficients:
## ar1 ar2 ar3
## -0.8424 -0.7260 -0.4724
## s.e. 0.1183 0.1313 0.1236
##
## sigma^2 = 35220: log likelihood = -438.29
## AIC=884.59 AICc=885.24 BIC=893.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,0) errors
##
## Coefficients:
## intercept xreg
## 471.3438 17.8651
## s.e. 204.5646 6.3520
##
## sigma^2 = 34928: log likelihood = -444.5
## AIC=895 AICc=895.38 BIC=901.61forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 835.7739 | 963.7221 | 804.6098 |
| Lo.80 | 962.5634 | 1093.8587 | 900.4450 |
| Point.Forecast | 1202.0745 | 1339.6925 | 1113.7263 |
| Hi.80 | 1441.5856 | 1585.5263 | 1379.8025 |
| Hi.95 | 1568.3752 | 1715.6629 | 1546.3794 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))